farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan
Abstract
Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ...
Read More
Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.
Maedeh Behifar; Mohsen Azadbakht; Farzaneh Hadadi; AliAkbar Matkan
Abstract
Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa ...
Read More
Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa farms due to the high chlorophyll content and high vegetation density; therefore, monitoring the changes of this plant is hindered. However, all indices do not perform similarly. In this research, the performance of different vegetation indices at different LAI values were investigated. The results showed that the CIgreen, CIrededge and NGRDI indices gained the best performance at high LAI values and they were less saturated. In contrast, the NDVI, NDREI and GNDI indices did not perform well and they were saturated at medium and high levels of LAI.
Farzaneh Hadadi; Hossain Aghighi; Ayoub Moradi
Volume 10, Issue 4 , February 2019, , Pages 99-120
Abstract
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate ...
Read More
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.
Farzaneh hadadi; Mohsen m_azadbakht; Maedeh Behifar; Hamid Salehi Shahrabi; amir moeinirad
Volume 10, Issue 3 , January 2019, , Pages 53-76
Abstract
Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, ...
Read More
Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the index had the highest correlation () and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.
